Cerebras Systems builds the world’s largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.
Cerebras’ current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

SKILLS AND QUALIFICATIONS

• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Mathematics, or a related field.
• 5+ years of experience in large-scale software engineering, with a focus on deep learning or related domains.
• Proficiency in Python for building and maintaining scalable systems.
• Advanced proficiency in C++, with an emphasis on multi-threaded programming, performance optimization, and system-level development.
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or JAX, and a strong understanding of their underlying architectures.
• Solid understanding of software architectural patterns for large-scale, high-performance applications.
• Proven experience leading and mentoring software or machine learning engineers.
• In-depth knowledge of machine learning algorithms, theory, and best practices for developing production-ready software.
• Strong problem-solving skills, with the ability to balance technical depth with practical implementation constraints.
• Exceptional communication and presentation skills, with the ability to work both independently and collaboratively across multidisciplinary teams.

ABOUT THE ROLE

The Inference ML team at Cerebras Systems is dedicated to enabling seamless integration of machine learning (ML) frameworks with our cutting-edge software and hardware ecosystem. Our mission is to empower developers and researchers to unlock the full potential of our platform, leveraging its performance, scalability, and flexibility. By bridging the gap between popular ML frameworks, like PyTorch, and our deeply optimized stack, we aim to provide tools that make developing and deploying ML models efficient and accessible. The team works closely with cross-functional groups, including hardware engineers, compiler developers, and product teams, to deliver high-impact solutions that redefine the boundaries of ML performance and usability.
As a Senior Software Engineer on the Inference ML team, you will play a key role in designing and implementing APIs and tools that simplify the process of running user-defined ML models on our platform. You will architect solutions that enable seamless model translation and execution, ensuring high throughput and low latency while maintaining ease of use. Your responsibilities will include collaborating with other engineering teams to enhance the developer experience, supporting a wide range of ML workloads, and laying the groundwork for future support of additional frameworks. This role offers an opportunity to shape the evolution of our ML ecosystem while tackling complex technical challenges at the intersection of machine learning, software, and hardware.

RESPONSIBILITIES

• Lead and provide technical guidance to a team of machine learning engineers working on complex machine learning integration projects.
• Design and implement scalable and efficient integrations with popular machine learning frameworks, such as PyTorch, while ensuring compatibility with future frameworks.
• Analyze the characteristics of various ML models to make informed design decisions for scalable, intuitive, and user-friendly APIs.
• Optimize software to accelerate ML model training and ensure high throughput and low latency during inference.
• Stay up-to-date with advancements in machine learning and deep learning, and apply state-of-the-art techniques to enhance our solutions.
• Evaluate trade-offs between different approaches, clearly articulate design choices, and develop detailed proposals for implementing new features.
• Build and maintain robust automated test suites to ensure software quality, performance, and reliability.
• Contribute to an agile team environment by delivering high-quality software and adhering to agile development practices.
• Collaborate with cross-functional teams, including compiler engineers, kernel developers, and system architects, to integrate ML capabilities seamlessly into our products and services.